'''simple docstring'''






import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers.models.esm.modeling_esmfold import EsmForProteinFolding







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			List[str], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Dict=13, UpperCamelCase__      :			Union[str, Any]=7, UpperCamelCase__      :			List[str]=False, UpperCamelCase__      :			List[Any]=True, UpperCamelCase__      :			str=False, UpperCamelCase__      :			Tuple=False, UpperCamelCase__      :			str=19, UpperCamelCase__      :			Tuple=32, UpperCamelCase__      :			Optional[Any]=5, UpperCamelCase__      :			int=4, UpperCamelCase__      :			str=37, UpperCamelCase__      :			List[str]="gelu", UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			int=5_12, UpperCamelCase__      :			int=16, UpperCamelCase__      :			str=2, UpperCamelCase__      :			Any=0.02, UpperCamelCase__      :			Any=3, UpperCamelCase__      :			Any=4, UpperCamelCase__      :			Union[str, Any]=None, )  ->			Tuple:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  seq_length
        _A    		=  is_training
        _A    		=  use_input_mask
        _A    		=  use_token_type_ids
        _A    		=  use_labels
        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  intermediate_size
        _A    		=  hidden_act
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  type_sequence_label_size
        _A    		=  initializer_range
        _A    		=  num_labels
        _A    		=  num_choices
        _A    		=  scope
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[Any]:
        _A    		=  ids_tensor([self.batch_size, self.seq_length], self.vocab_size     )

        _A    		=  None
        if self.use_input_mask:
             _A    		=  random_attention_mask([self.batch_size, self.seq_length]     )

        _A    		=  None
        _A    		=  None
        _A    		=  None
        if self.use_labels:
             _A    		=  ids_tensor([self.batch_size], self.type_sequence_label_size     )
             _A    		=  ids_tensor([self.batch_size, self.seq_length], self.num_labels     )
             _A    		=  ids_tensor([self.batch_size], self.num_choices     )

        _A    		=  self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Dict:
        _A    		=  EsmConfig(
            vocab_size=33, hidden_size=self.hidden_size, pad_token_id=1, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, is_folding_model=UpperCamelCase__, esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}, )
        return config
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			str, UpperCamelCase__      :			Dict, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			int     )  ->			List[str]:
        _A    		=  EsmForProteinFolding(config=UpperCamelCase__     ).float()
        model.to(UpperCamelCase__     )
        model.eval()
        _A    		=  model(UpperCamelCase__, attention_mask=UpperCamelCase__     )
        _A    		=  model(UpperCamelCase__     )
        _A    		=  model(UpperCamelCase__     )

        self.parent.assertEqual(result.positions.shape, (8, self.batch_size, self.seq_length, 14, 3)     )
        self.parent.assertEqual(result.angles.shape, (8, self.batch_size, self.seq_length, 7, 2)     )

   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        _A    		=  self.prepare_config_and_inputs()
        (
            (
            _A
        )		,       (
            _A
        )		,       (
            _A
        )		,       (
            _A
        )		,       (
            _A
        )		,       (
            _A
        )		,       
        )    		=  config_and_inputs
        _A    		=  {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_torch
class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   False

   __lowerCAmelCase         =   (EsmForProteinFolding,) if is_torch_available() else ()
   __lowerCAmelCase         =   ()
   __lowerCAmelCase         =   {} if is_torch_available() else {}
   __lowerCAmelCase         =   False
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Dict:
        _A    		=  EsmFoldModelTester(self     )
        _A    		=  ConfigTester(self, config_class=UpperCamelCase__, hidden_size=37     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        self.config_tester.run_common_tests()
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			str:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCamelCase__     )
   @unittest.skip('Does not support attention outputs'     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Tuple:
        pass
   @unittest.skip
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Union[str, Any]:
        pass
   @unittest.skip('Esm does not support embedding resizing'     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Any:
        pass
   @unittest.skip('Esm does not support embedding resizing'     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Any:
        pass
   @unittest.skip('ESMFold does not support passing input embeds!'     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Tuple:
        pass
   @unittest.skip('ESMFold does not support head pruning.'     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[Any]:
        pass
   @unittest.skip('ESMFold does not support head pruning.'     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Union[str, Any]:
        pass
   @unittest.skip('ESMFold does not support head pruning.'     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        pass
   @unittest.skip('ESMFold does not support head pruning.'     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        pass
   @unittest.skip('ESMFold does not support head pruning.'     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[int]:
        pass
   @unittest.skip('ESMFold does not output hidden states in the normal way.'     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Union[str, Any]:
        pass
   @unittest.skip('ESMfold does not output hidden states in the normal way.'     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[Any]:
        pass
   @unittest.skip('ESMFold only has one output format.'     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Any:
        pass
   @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality'     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Union[str, Any]:
        pass
   @unittest.skip('ESMFold does not support input chunking.'     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			int:
        pass
   @unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			str:
        pass
   @unittest.skip('ESMFold doesn\'t support torchscript compilation.'     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			str:
        pass
   @unittest.skip('ESMFold doesn\'t support torchscript compilation.'     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Tuple:
        pass
   @unittest.skip('ESMFold doesn\'t support torchscript compilation.'     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        pass
   @unittest.skip('ESMFold doesn\'t support data parallel.'     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        pass

   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        pass







@require_torch
class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			str:
        _A    		=  EsmForProteinFolding.from_pretrained('facebook/esmfold_v1'     ).float()
        model.eval()
        _A    		=  torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]     )
        _A    		=  model(UpperCamelCase__     )['positions']
        _A    		=  torch.tensor([2.5_828, 0.7_993, -10.9_334], dtype=torch.floataa     )
        self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0], UpperCamelCase__, atol=1e-4     )     )




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a    :	Union[str, Any]         =    1_6
__a    :	int         =    3_2





def 							UpperCAmelCase		(       lowercase				,     lowercase = 16				):







				"""simple docstring"""



				__lowercase					     =				AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				__lowercase					     =				load_dataset('''glue'''				,     '''mrpc'''				)

				def tokenize_function(lowercase				):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase					     =				tokenizer(examples['''sentence1''']				,     examples['''sentence2''']				,     truncation=lowercase				,     max_length=lowercase				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__lowercase					     =				datasets.map(
								    lowercase				,     batched=lowercase				,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase					     =				tokenized_datasets.rename_column('''label'''				,     '''labels'''				)

				def collate_fn(lowercase				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__lowercase					     =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__lowercase					     =				16
								elif accelerator.mixed_precision != "no":
												__lowercase					     =				8
								else:
												__lowercase					     =				None

								return tokenizer.pad(
								    lowercase				,     padding='''longest'''				,     max_length=lowercase				,     pad_to_multiple_of=lowercase				,     return_tensors='''pt'''				,     )

				# Instantiate dataloaders.
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''train''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''validation''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__a    :	List[Any]         =    mocked_dataloaders  # noqa: F811





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     lowercase				) == "1":
								__lowercase					     =				2
				# Initialize accelerator
				__lowercase					     =				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase					     =				config['''lr''']
				__lowercase					     =				int(config['''num_epochs''']				)
				__lowercase					     =				int(config['''seed''']				)
				__lowercase					     =				int(config['''batch_size''']				)

				__lowercase					     =				evaluate.load('''glue'''				,     '''mrpc'''				)

				# If the batch size is too big we use gradient accumulation
				__lowercase					     =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__lowercase					     =				batch_size // MAX_GPU_BATCH_SIZE
								__lowercase					     =				MAX_GPU_BATCH_SIZE

				set_seed(lowercase				)
				__lowercase     ,	__lowercase					     =				get_dataloaders(lowercase				,     lowercase				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase					     =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=lowercase				)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__lowercase					     =				model.to(accelerator.device				)

				# Instantiate optimizer
				__lowercase					     =				AdamW(params=model.parameters()				,     lr=lowercase				)

				# Instantiate scheduler
				__lowercase					     =				get_linear_schedule_with_warmup(
				    optimizer=lowercase				,     num_warmup_steps=100				,     num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				accelerator.prepare(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				)

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.loss
												__lowercase					     =				loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowercase					     =				0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												with torch.no_grad():
																__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.logits.argmax(dim=-1				)
												__lowercase     ,	__lowercase					     =				accelerator.gather((predictions, batch['''labels'''])				)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(lowercase				) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowercase					     =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
																				__lowercase					     =				references[: len(eval_dataloader.dataset				) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=lowercase				,     references=lowercase				,     )

								__lowercase					     =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,     type=lowercase				,     default=lowercase				,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,     help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''				,     )
				parser.add_argument('''--cpu'''				,     action='''store_true'''				,     help='''If passed, will train on the CPU.'''				)
				__lowercase					     =				parser.parse_args()
				__lowercase					     =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase				,     lowercase				)


if __name__ == "__main__":
		main()